# Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events

arXiv: https://arxiv.org/abs/1911.04379 · HTML: https://mlsanity.com/paper/1911.04379/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions](https://mlsanity.com/paper/2407.14850/) (2024)
- [Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping](https://mlsanity.com/paper/2506.16243/) (2025)
- [Imagined Speech State Classification for Robust Brain-Computer Interface](https://mlsanity.com/paper/2412.12215/) (2024)
- [Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration](https://mlsanity.com/paper/2511.13739/) (2025)
- [Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP](https://mlsanity.com/paper/2604.01653/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.